diff --git a/python/sglang/srt/layers/moe/topk.py b/python/sglang/srt/layers/moe/topk.py
index ae6f50930..e21e5f510 100644
--- a/python/sglang/srt/layers/moe/topk.py
+++ b/python/sglang/srt/layers/moe/topk.py
@@ -1238,6 +1238,38 @@ def biased_grouped_topk_gpu(
                 renormalize,
                 scaling,
             )
+        elif (
+            _is_xpu
+            and num_expert_group == 1
+            and topk_group == 1
+            and num_fused_shared_experts == 0
+            and num_experts <= 256
+            and topk <= 8
+        ):
+            if not apply_routed_scaling_factor_on_output:
+                scaling = 1.0
+
+            num_tokens = gating_output.shape[0]
+
+            topk_values = torch.empty(
+                (num_tokens, topk), dtype=torch.float32, device=gating_output.device
+            )
+            topk_indices = torch.empty(
+                (num_tokens, topk), dtype=torch.int32, device=gating_output.device
+            )
+
+            if num_tokens == 0:
+                return topk_values, topk_indices
+
+            topk_sigmoid(
+                topk_values,
+                topk_indices,
+                gating_output,
+                renormalize,
+                correction_bias,
+            )
+            return topk_values * scaling, topk_indices
+
         else:
             return biased_grouped_topk_impl(
                 hidden_states,
diff --git a/test/registered/xpu/test_topk.py b/test/registered/xpu/test_topk.py
new file mode 100644
index 000000000..2ddc0be59
--- /dev/null
+++ b/test/registered/xpu/test_topk.py
@@ -0,0 +1,101 @@
+import unittest
+
+import torch
+
+from sglang.srt.layers.moe.topk import (
+    biased_grouped_topk_gpu,
+)
+from sglang.srt.layers.moe.topk import (
+    biased_grouped_topk_impl as native_biased_grouped_topk,
+)
+from sglang.test.ci.ci_register import register_xpu_ci
+from sglang.test.test_utils import CustomTestCase
+
+register_xpu_ci(est_time=5, suite="stage-b-test-1-gpu-xpu")
+
+
+# Nemotron-3 uses biased_grouped_topk
+class TestBiasedGroupedTopK(CustomTestCase):
+    def _run_single_test(
+        self,
+        M,
+        E,
+        G,
+        topk,
+        topk_group,
+        renormalize,
+        gating_dtype,
+        bias_dtype,
+        routed_scaling_factor,
+    ):
+        torch.manual_seed(1024)
+        device = torch.device("xpu")
+
+        # expand gating_output by M, otherwise bfloat16 fall into same value aftering truncating
+        hidden_states = torch.randn(M, 100, dtype=torch.bfloat16, device=device)
+        gating_output = torch.randn(M, E, dtype=gating_dtype, device=device)
+        correction_bias = torch.randn(E, dtype=bias_dtype, device=device)
+
+        ref_topk_weights, ref_topk_ids = native_biased_grouped_topk(
+            hidden_states,
+            gating_output,
+            correction_bias,
+            topk,
+            renormalize,
+            G,
+            topk_group,
+            routed_scaling_factor=routed_scaling_factor,
+        )
+
+        # fused version
+        topk_weights, topk_ids = biased_grouped_topk_gpu(
+            hidden_states,
+            gating_output,
+            correction_bias,
+            topk,
+            renormalize,
+            G,
+            topk_group,
+            0,
+            routed_scaling_factor,
+            None,
+        )
+
+        res = torch.zeros(M, E, dtype=torch.float, device=device)
+        ref = torch.zeros(M, E, dtype=torch.float, device=device)
+        res.scatter_(1, topk_ids.long(), topk_weights)
+        ref.scatter_(1, ref_topk_ids.long(), ref_topk_weights)
+        torch.testing.assert_close(res, ref)
+
+    # Nemotron-3-Nano-30B-A3B uses fast biased_grouped_topk with num_expert_group = 1 and topk_group = 1
+    def test_fast_biased_grouped_topk(self):
+        # The test config is also from this nemotron model.
+        E_num = 128
+        num_expert_group = 1
+        topk_value = 6
+        topk_group = 1
+        gating_dtype = torch.bfloat16
+        bias_dtype = torch.float32
+        renormalize = True
+        routed_scaling_factor = 2.5
+
+        bs = [1, 2, 4, 8]
+        seq_len = 1024
+        num_tokens = [b * seq_len for b in bs]
+
+        for M in num_tokens:
+            self._run_single_test(
+                M,
+                E_num,
+                num_expert_group,
+                topk_value,
+                topk_group,
+                renormalize,
+                gating_dtype,
+                bias_dtype,
+                routed_scaling_factor,
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()